TreeCANN - k-d tree coherence approximate nearest neighbor algorithm

Igor Olonetsky, Shai Avidan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

TreeCANN is a fast algorithm for approximately matching all patches between two images. It does so by following the established convention of finding an initial set of matching patch candidates between the two images and then propagating good matches to neighboring patches in the image plane. TreeCANN accelerates each of these components substantially leading to an algorithm that is ×3 to ×5 faster than existing methods. Seed matching is achieved using a properly tuned k-d tree on a sparse grid of patches. In particular, we show that a sequence of key design decisions can make k-d trees run as fast as recently proposed state-of-the-art methods, and because of image coherency it is enough to consider only a sparse grid of patches across the image plane. We then develop a novel propagation step that is based on the integral image, which drastically reduces the computational load that is dominated by the need to repeatedly measure similarity between pairs of patches. As a by-product we give an optimal algorithm for exact matching that is based on the integral image. The proposed exact algorithm is faster than previously reported results and depends only on the size of the images and not on the size of the patches. We report results on large and varied data sets and show that TreeCANN is orders of magnitude faster than exact NN search yet produces matches that are within 1% error, compared to the exact NN search.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Pages602-615
Number of pages14
EditionPART 4
DOIs
StatePublished - 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: 7 Oct 201213 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume7575 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th European Conference on Computer Vision, ECCV 2012
Country/TerritoryItaly
CityFlorence
Period7/10/1213/10/12

Keywords

  • Approximate nearest neighbor search
  • patch matching

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